Advanced Computer Vision Training Course

Introduction:

Computer Vision is a field of Artificial Intelligence and Computer Science that aims at giving computers a visual understanding of the world. Computer Vision has been used in face recognition, autonomous cars, image search, optical character recognition, robotics vision, machine vision, and many applications.

Computer Vision is undergoing rapid advances in recent years. In this course, you will learn state of the art computer vision techniques such Region-based CNN and YOLO techniques. You will also learn about convolutional neural network and transfer learning.

Course Objectives:

  • ConvNET
  • Transfer Learning
  • R-CNN Based Object Detection
  • YOLO Based Object Detection

Duration

7 hours, 1 Day Course

Mode of Delivery

Classroom-based, Instructor-led Training

Course Outlines

  1. Overview of Computer Vision
    1. Computer Vision Tasks
    2. Computer Vision Applications
    3. Setup Google Colab for Keras
  2. ConvNet
    1. What is ConvNET?
    2. ConvNET Architecture
    3. Image Classification for HandWritten Digits
    4. Image Classification for Cats and Dogs Small Dataset
    5. ImageDataGenerator
    6. Fit Generator
    7. Overfitting Issue
    8. Dropout & Data Augmentation
    9. Mini Project – Convnet with User Own Images
  3. Transfer Learning
    1. What is Transfer Learning
    2. Transfer Learning with VGG16 Model
    3. Fine Tuning VGG16 Model for Cats and Dogs Small Dataset
    4. Mini Project – Transfer Learning with User Own Images
  4. R-CNN
    1. R-CNN
    2. Fast R-CNN
    3. Faster R-CNN
    4. Mask R-CNN
    5. Mask R-CNN Demo
  5. YOLO
    1. What is YOLO
    2. YOLO Algorithm
    3. Anchor Boxes
    4. IOU
    5. Non Max Suppression
    6. YOLO v3 Demo